• 2020-01-09 16:16:22
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原文:Das S , Meher P K , Rai A , et al. Statistical Approaches for Gene Selection, Hub Gene Identification and Module Interaction in Gene Co-Expression Network Analysis: An Application to Aluminum Stress in Soybean (Glycine max L.)[J]. Plos One, 2017, 12(1):e0169605.


In order to understand the interrelationship among the selected genes, identification of gene modules and key genes responsible for a particular stress/condition, analysis of gene co-expression networks need to be carried out. Weighted Gene Co-expression Network Analysis (WGCNA) is a latest and popular technique used to decipher co-expression patterns among genes. The WGCNA approach typically deals with the identification of gene modules by using the gene expression levels that are highly correlated across samples . This technique has been successfully utilized to detect gene modules in Arabidopsis, rice, maize and poplar for various biotic and abiotic stresses . Further, this approach also leads to construction of Gene Co-expression Network (GCN), a scale free network, where, genes are represented as nodes and edges depict associations among genes . In such network, highly connected genes are called hub genes, which are expected to play an important role in understanding the biological mechanism of response under stresses/conditions. Identification of hub genes will also help in mitigating the stress in plants through genetic engineering. The existing approaches have mainly focused on hub gene identification, based only on gene connection degrees in the GCN. Moreover, these techniques select such genes empirically without any statistical criteria. Besides, few approaches can be found in the literature for the identification of hub nodes in a scale free network。


为了理解所选基因之间的相互关系,鉴定引起特定胁迫/条件的基因模块和关键基因,需要进行基因共表达网络的分析。加权基因共表达网络分析(WGCNA)是一种最新的流行技术,用于破译基因之间的共表达模式。 WGCNA方法通常通过使用在样品之间高度相关的基因表达水平来进行基因模块的鉴定。该技术已成功用于检测拟南芥,水稻,玉米和杨树中各种生物和非生物胁迫的基因模块。此外,这种方法还会构建基因共表达网络(Gene Co-expression Network,GCN),这是一种无标度的网络,其中,基因表示为节点,边缘描绘了基因之间的关联。在这种网络中,高度连接的基因称为hub基因,有望在理解胁迫/条件下的应答生物学机制中发挥重要作用。Hub基因的鉴定也将有助于通过基因工程减轻植物的压力。


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